{
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  "Type": "Package",
  "Title": "PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and\nfeature selection of omics data",
  "Version": "1.45.0",
  "Date": "2024-09-13",
  "Authors@R": "person(\"Etienne A.\", \"Thevenot\", email = \"etienne.thevenot@cea.fr\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-1019-4577\"))",
  "biocViews": "Regression, Classification, PrincipalComponent,\nTranscriptomics, Proteomics, Metabolomics, Lipidomics,\nMassSpectrometry, ImmunoOncology",
  "Description": "Latent variable modeling with Principal Component Analysis\n(PCA) and Partial Least Squares (PLS) are powerful methods for\nvisualization, regression, classification, and feature\nselection of omics data where the number of variables exceeds\nthe number of samples and with multicollinearity among\nvariables. Orthogonal Partial Least Squares (OPLS) enables to\nseparately model the variation correlated (predictive) to the\nfactor of interest and the uncorrelated (orthogonal) variation.\nWhile performing similarly to PLS, OPLS facilitates\ninterpretation. Successful applications of these chemometrics\ntechniques include spectroscopic data such as Raman\nspectroscopy, nuclear magnetic resonance (NMR), mass\nspectrometry (MS) in metabolomics and proteomics, but also\ntranscriptomics data. In addition to scores, loadings and\nweights plots, the package provides metrics and graphics to\ndetermine the optimal number of components (e.g. with the R2\nand Q2 coefficients), check the validity of the model by\npermutation testing, detect outliers, and perform feature\nselection (e.g. with Variable Importance in Projection or\nregression coefficients). The package can be accessed via a\nuser interface on the Workflow4Metabolomics.org online resource\nfor computational metabolomics (built upon the Galaxy\nenvironment).",
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  "URL": "https://doi.org/10.1021/acs.jproteome.5b00354",
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  "Packaged": {
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:41:32 UTC",
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  "Author": "Etienne A. Thevenot [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-1019-4577>)",
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      "title": "PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data",
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        "ropls"
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